The Toil Toward Quantum Computers Just Turned Into a Sprint

A new optical chip that can process photons in a dizzying number of infinite ways has been developed by two research teams. Researchers from the University of Bristol in the UK and Nippon Telegraph and Telephone in Japan (NTT) are behind the breakthrough in quantum computing.

The means to solve daunting problems such as the ability to design new life-saving drugs; perform advanced calculations that are a step or two beyond even supercomputers; and analyze weather patterns for more accurate forecasting has just received a major boost.

A group of researchers have pulled off a staggering feat; they’ve developed a silicon-based optical chip that is fully reprogrammable and can process photons in every way imaginable and then some, reports Phys.org.

Prof. Jeremy O’Brien, the Director of the Centre for Quantum Photonics at Bristol University where researchers masterminded the development of the chip, said:

Over the last decade, we have established an ecosystem for photonic quantum technologies, allowing the best minds in quantum information science to hook up with established research and engineering expertise in the telecommunications industry.

”It’s a model that we need to encourage if we are to realize our vision for a quantum computer,” he added.

The chip has essentially validated a vast array of existing quantum research and experiments. It also opens the doors for future ideas and protocols in quantum mechanics that haven’t even been conceived yet, bringing yet more focus to the staggering potential of quantum computers.

Quantum Computers and the Need for It

Quantum computers are advanced, sci-fi standard machines devised and founded on the principles of quantum mechanics. These computers will be far more powerful than the supercomputers of today. Computer science is on the very cusp of a technological milestone if the computers of tomorrow are based on quantum mechanics principles, making for an evolutionary leap in computing technology.

Several futurologists predict that:

The widespread use and implementation of quantum computers in general industry is only a decade or two away.

The United States is expected to be the leader in the quantum technology arena, with forecasts predicting China to be the closest competitor.

Countries and corporations that gain initial access to quantum computers will have a decisive edge over others in any technological space.

The implications of quantum computers in geopolitics and national security will be staggering.

Stepping beyond military and security circles, quantum computers represent advancements in science – the likes of which could lead to saving millions of lives – thanks to faster research and development of new drugs. Rapid strides are expected to be made towards the development of artificial intelligence. While today’s algorithms used in developing algorithms depend on pattern recognition, quantum computing brings the very real plausibility of machines adapting to anomalous scenarios. Non-routine tasks can be undertaken by machines thanks to quantum computing aided advanced algorithms. Self-driven cars are already a reality and the research into automation through quantum computing will push the envelope further.

“Quantum computing could allow scientists to calculate molecular structure, which means they would then be able to control and design molecules, or compute more efficient industrial reaction chains,” said Jonathan Home, a scientist at the Institute for Quantum Mechanics in Switzerland.

But in all likelihood, we’ll use quantum computers for applications we never dreamed of.

With all the potential a quantum computer represents, getting to actually build one is a complicated task. The quantum technology industry is still relatively in its infancy. A major hurdle in testing and understanding new theories for quantum science and computing is the resources and time required to build new experiments suited for the task. Quantum systems and architecture are notoriously fragile, making quantum research appear to be in a constant state of flux with the strides being made.

The Breakthrough Chip

Despite the challenges, the development of the new silicon chip marks a change for experiments with photons and shines the spotlight on the future of quantum technology research.

Dr. Anthony Laing, the leader of the project at the University of Bristol said:

A whole field of research has essentially been put onto a single optical chip that is easily controlled. The implications of the work go beyond the huge resource savings. Now anybody can run their own experiments with photons, much like they operate any other piece of software on a computer. They no longer need to convince a physicist to devote many months of their life to painstakingly build and conduct a new experiment.

The research team were able to ably demonstrate the chip’s unique traits by re-programming it to perform a number of differing experiments. Without the chip, each experiment would have previously taken months to put together and implement.

Team member Jacques Carolan, a student at the University, noted: “Once we wrote the code for each circuit, it took seconds to re-programme the chip, and milliseconds for the chip to switch to the new experiment. We carried out a year’s worth of experiments in a matter of hours. What we’re really excited about is using these chips to discover new science that we haven’t even thought of yet.”

Today, the University of Bristol’s ‘Quantum in the Cloud’ service is a pioneering, one-of-a-kind service that allows a quantum processor to be publicly accessible. The research team plans on adding more breakthrough chips like the one they’ve just invented to help others discover and research the world of quantum mechanics on their own.

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Quantum optical processor using photons as superstate process is past many month now, We can hold 8 plus 8 in a single Qubit of data. and by alter processors architecture get different functions like Calculation, compression, and also on the global patent application a one bit BQB binary in quantum processing and binary out. and NTT /Mr Hando and ID quantique in swizz has been interested from the start.

The only advancement here seems to be reconfiguration capabilities that enable faster implementation of quantum experiments. Are there actually any new processing capabilities, such as increasing the number of qubits sufficiently to solve some real-world problems, not toy samples? Breaking RSA cryptography, for example, requires on the order of many thousands of qubits, far beyond the few demonstrated in the lab years ago with no subsequent progress. That’s really the holy grail, along with the $64 trillion question of whether the physics of this universe even allows it in practice.

I wish the article contained more information about the chip and it’s specific implications, rather than lengthy and general praise for quantum computers. BTW, the D-Wave is considered a quantum computer, and is operating in the 100 teraflop arena. Both Google and NASA are using it now in their jointly owned lab.

I wish to add that this might be a very exciting development (the so-called photon chip), but it just can’t be ascertained by reading this article. The Singularity Loop is intelligence improving technology, and technology increasing intelligence.

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YEXT: An Invisible Force In Artificial Intelligence

YEXT, Inc. (NYSE: YEXT) is one of those behind the scenes companies involved in Intelligence Search that plays an important role in Artificial Intelligence. What does that mean? Remember the Amazon commercial? “Eco, order a 12” Pizza with pepperoni from Stromboli’s and have it delivered”.

Today the vast majority of online searches go through third-party sources such as data aggregators, governmental agencies and consumers. The net result of this third party sourcing has been to produce “best guess” data that can often miss or misstate the target data field.

YEXT developed a better way to source critical digital knowledge. For example business clients use YEXT to update public facts about their brands. They are building their based on the rapid and ever changing nature of data. So far the YEXT Knowledge Network offers over 100 services to more than 110 corporate clients and has over $150 million in annual revenue. So could YEXT play a key role in AI, the next big thing?

How YEXT Works

Most of us are familiar with big time search engines like Google, Google Maps, Facebook, Instagram, Bing, Cortana, Apple Maps, Siri and Yelp. These pioneering companies are the major drivers in information search today. However, we also know, their accuracy is not exactly ideal.

This is where YEXT steps in. Their knowledge engine platform lets business manage their digital knowledge in the cloud and sync it to over 100 services including the kingpins of search noted above.

Intelligent Search is the structured information that a business wants to make publicly accessible. In food service it could be the address, phone number or menu details of a restaurant; in healthcare, the health insurances accepted by a physician or the precise drop-off point of the emergency room at a hospital campus; or in finance, the ATM locations, retail bank holiday hours or insurance agent biographies.

Artificial Intelligence Offers a Potential $10 Billion Market

Improving search results in general is nice but not very sexy. It doesn’t make you want to beg for more information. However, when you consider the role of Artificial Intelligence (AI) in our evermore data intense world, the importance of Intelligent Search and the opportunities for YEXT becomes a compelling story.

The AI trend is already underway as YEXT is increasingly using the structured data on their platform to expand or add new integrations with vertically specialized applications, voice-based search and AI engines.

Just Right For Big Data Applications

YEXT customers use their platform to manage their digital knowledge covering over 17 million attributes and nearly one million locations. These customers include leading businesses in a diverse set of industries, such as healthcare and pharmaceuticals, retail, financial services, manufacturing and technology.

Management believes the market for digital knowledge management is large and mostly untapped with over 100 million potential business locations and points of interest in the world equaling over $10 billion.

Shooting For Acquisitions and Broad AI Penetration

Founded in 2006 by serial entrepreneurs Howard Lerman (CEO) and Brian Distelburger, President these two are typical software guys whose vision appears much more broad based the their current focus with YEXT. Here is where the prospectus from their April 2017 IPO offers some mystery and excitement to the story.

Unlike most rapid growth tech companies YEXT had no urgent need to go public. They generated almost $60 million in gross profit in 2016 before heavy marketing costs resulted in a loss of $26.5 million. Even so, they still ended the year with $20 million in cash. That’s a fair distance from being destitute.

The company’s real need for the IPO was to establish a liquid public market for the stock. They raised about $123.5 million, all of which will go into the bank. The company is debt free and there are no insiders selling stock. Very interesting.

Strong Financial Results

For the latest reported nine months ended October 31, 2017 revenues grew 38% reaching $122 million. The good news is the gross profits reached a record 75% or $90 million. All of this was spent on sales and marketing to expand the business. When all the beans were counted, YEXT lost $50 million producing a $30 million negative cash flow. The balance sheet remains liquid with $120+ million in cash and securities.

FYI: In spite of some top notch bankers underwriting its IPO and analysts from those same five firms covering the company, the stock has done almost nothing for investors. This $1.1 billion market cap was recently hanging out around $12 about the same as the IPO price.

Featured image courtesy of Shutterstock.

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4.4 stars on average, based on 76 rated postsJames Waggoner is a veteran Wall Street analyst and hedge fund manager who has spent the past few years researching the fintech possibilities of cryptocurrencies. He has a special passion for writing about the future of crypto.

The End of Human Money Managers

Quantitative Easing by central banks around the world has led to dramatic changes in the money management industry over the past six years. Not only have we seen increasing regional differences, but stock picking has also become more difficult as the money injected into the markets by central banks has lifted pretty much everything, regardless of valuation and the future potential of the asset.

Investors have become impatient and highly demanding as a result of years of low interest rates. Old mutual funds are being swapped out for new, better ones at a record pace as investors hunt for higher ROI. Passive income has become a trend, and ETF’s and automated investment strategies are getting more and more popular as a result.

How do money managers attract capital?

There are three main factors that determine how much capital a money manager is able to attract from investors:

Track record

Strategy

Technology

Changes in any of these factors can have a big impact on investors’ willingness to let the fund manager keep the money they have already invested with him, or receive new money.

Technology has been a very important driver over the past few years. Data-driven, or quantitative funds are gaining an ever-increasing market share in the money management space. This is happening because more and more people are realizing the obvious benefits that this type of money management has to offer.

Investors increasingly prefer the robustness, speed, and predictability that automated money management can provide. When it comes to robustness, we are referring to both the physical and psychological aspect of it.

Humans vs. robots

Humans are pretty much the opposite of “robust,” in the true sense of the word. Our emotional state on any given day can make us react to things in different ways than we otherwise would have done, potentially leading to critical mistakes for a trader.

As humans, we may miss trading opportunities in the market because we came in late, took a day off, or simply didn’t pay attention at any given moment.

Robots are obviously not affected by fatigue and lack of focus. For example, a robot can monitor the stock or cryptocurrency market and trade just like a human trader would do, with the only difference being that the former (arguably) does it better and never needs to rest.

Thanks to the high computing power available today, robots can collect, verify, analyze, and react to opportunities long before a human will even understand that such opportunities exist.

Data-driven approach to fund management is taking over

A recent ranking by Institutional Investor Magazine revealed that out of the world’s 100 biggest hedge funds, five of the top six spots were held by data-driven funds.

On first place was Ray Dalio’s Bridgewater Associates with $122.3 billion under management. In 2016, Bridgewater grew the amount of money under management by 17%.

Renaissance Technologies, the company known for having hundreds of mathematicians, physicists, and coders on their payroll, came in fourth with $43 billion.

Two Sigma, which is also well-known for using technologies like AI and machine learning, came in fifth with $39 billion under management. Their increase from the year before was 28%.

According to Barclays, $500 billion are now invested in purely data-driven funds, while JP Morgan claims that data-driven trading strategies accounts for a whopping 90% of global trading volumes in stocks.

The core objective of any money manager is always to follow the money. That’s why we are seeing a race right now by the big players in the industry to use words like “technology-driven,” “artificial intelligence,” and so on. Whether or not that is true is not always a concern for them.

Money managers are destined to unemployment

Those who are really in trouble because of this huge change are the money managers themselves. Most of them will likely lose their jobs over the next few years. There is simply very little need for their very expensive services anymore, as robots are able to do the same thing in a much cheaper and more consistent way.

As legendary investors Jim Rogers predicted a few years ago, the stock brokers will become broke and the farmers are going to be driving Lamborghinis. Maybe there will finally be some truth to this.

Featured image from Pixabay.

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4.3 stars on average, based on 32 rated postsFredrik Vold is an entrepreneur, financial writer, and technical analysis enthusiast. He has been working and traveling in Asia for several years, and is currently based out of Beijing, China. He closely follows stocks, forex and cryptocurrencies, and is always looking for the next great alternative investment opportunity.

Bitcoin Giant Bitmain Enters the High Stakes AI Race

The Sophon, named for a fictional proton-sized supercomputer, could be the tool to train neural networks in data centers worldwide. It is the latest project being developed by Bitmain Technologies Ltd., the bitcoin mining giant that has carved out a dominant position in bitcoin mining.

Such chips, called application-specific integrated circuits (ASICs), could unleash a new wave of distributed computing, according to Michael Bedford Taylor, a University of Washington professor who studies bitcoin mining and chips.

Sophon is due to debut before the end of the year.

Bitmain Has The Know-How

Bitmain has the background to play a role in the expanding artificial intelligence industry. The company designs the silicon that goes in bitcoin mining equipment, assembles the machines and sells them worldwide, in addition to its own bitcoin mining operation and the ones that it manages for other mining pools.

Jihan Wu, the co-founder of Bitmain, supports the New York Agreement that seeks to double the bitcoin block size under the SegWit2X proposal, a move that some in the bitcoin community view as an attempt to give the miners control over bitcoin.

Some also believe Wu was behind the recent bitcoin split known as bitcoin cash, which at least one of Bitmain’s miners supported, a contention that Wu has denied. Wu points out that he was among the supporters of Bitcoin Unlimited, an earlier bitcoin scaling proposal that did not get activated.

Why Wu Supports Forks

Wu nonetheless said splits should be allowed. He said a fork is inevitable since people in the bitcoin community do not agree on how to best scale bitcoin.

Wu met Micree Zhan, Bitcoin’s co-founder, when Zhan was running DivaIP in 2010, a company that made a device that allowed a user to stream a TV show on a computer screen.

In 2011, Wu needed a chip designer to build a mining operation and approached Zhan. Zhan first designed an ASIC to run SHA-256, the cryptographic calculation used in bitcoin, at maximum efficiency. It took him six months to finish the job. His first rig, Antminer S1, was ready in November 2013.

Bitmain felt the sting of the 2014 Mt. Gox meltdown. But by 2015, bitcoin’s price bottomed out and later recovered. In the meantime, Bitmain introduced its Antminer S5.

Ready To Take On Google

Bitmain has since developed a deep learning chip with improved efficiency. Users will be able to build their own models on the ASICs, enabling neural networks to deliver results at a faster pace. Google’s DeepMind unit used this technique to train its AlphaGo artificial intelligence.

Bitmain plans to sell the chips to any company looking to train its own neural nets, including firms like Alibaba, Tencent and Baidu. Bitmain could build its own data centers with thousands of deep learning rigs, renting out the computation power to clients the way it does with bitcoin mines.

Professor Taylor said companies like Bitmain that have excelled in bitcoin mining could take on the Googles and Nvidias since they have developed the skills to survive in an ultra-competitive and highly commoditized industry, and have the system level design expertise and the ability to reduce data center costs.

Important: Never invest (trade with) money you can't afford to comfortably lose. Always do your own research and due diligence before placing a trade. Read our Terms & Conditions here. Trade recommendations and analysis are written by our analysts which might have different opinions. Read my 6 Golden Steps to Financial Freedom here. Best regards, Jonas Borchgrevink.

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3.9 stars on average, based on 8 rated postsLester Coleman is a veteran business journalist based in the United States. He has covered the payments industry for several years and is available for writing assignments.

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